Five-point Fundamental Matrix Estimation for Uncalibrated Cameras

TL;DR

This paper introduces a minimal solver for estimating the fundamental matrix from five correspondences using a novel approach that combines homography estimation from three co-planar points with two additional points, improving accuracy and efficiency.

Contribution

The paper presents a new minimal solver that estimates the fundamental matrix from five correspondences by leveraging homography from three co-planar points and two additional points, enhancing robustness and speed.

Findings

Outperforms state-of-the-art algorithms in accuracy and iteration count.

Effective on both synthetic and real image data.

Improves multi-motion estimation accuracy in two-view setups.

Abstract

We aim at estimating the fundamental matrix in two views from five correspondences of rotation invariant features obtained by e.g.\ the SIFT detector. The proposed minimal solver first estimates a homography from three correspondences assuming that they are co-planar and exploiting their rotational components. Then the fundamental matrix is obtained from the homography and two additional point pairs in general position. The proposed approach, combined with robust estimators like Graph-Cut RANSAC, is superior to other state-of-the-art algorithms both in terms of accuracy and number of iterations required. This is validated on synthesized data and $561$ real image pairs. Moreover, the tests show that requiring three points on a plane is not too restrictive in urban environment and locally optimized robust estimators lead to accurate estimates even if the points are not entirely co-planar. As a potential application, we show that using the proposed method makes two-view multi-motion estimation more accurate.